Multi-task Learning.
Multi-task learning has emerged as a promising approach for sharing structure across multiple tasks to enable more efficient learning. Multi-task deep metric learning (MDML) has also recently been shown to be advantageous, allowing to learn the metrics for several related tasks jointly [1]}, [2]}. In MDML, concurrently solving different tasks is also employed by classical multi-task learning and often based on a divide-and-conquer principle with multiple learners optimizing a given subtask [2]}. For example, [4]} utilizes additional training data and annotations to capture extra information, while our IDEAL is defined on standard training data only. In practice, multi-task learning presents a challenging optimization problem, which has been tackled in several ways in prior work. A number of architectural solutions have been proposed to the multi-task learning problem based on multiple modules or paths [5]}. It inspired us to formulate various augmented domains to create multiple auxiliary learning tasks, and employ an independent branch for each domain.
